Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals

Vivó-Truyols, Gabriel; Torres-Lapası́o, J.R.; Nederkassel, A.M. van; Heyden, Yvan Vander; Massart, D.L.

doi:10.1016/j.chroma.2005.03.092

Cited by 136 publications

(72 citation statements)

References 16 publications

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“…Another way for normalization is to determine a samplewise bias factor from the data themselves. 13 It is not uncommon to attempt to find affected pathways from metabolic profiles when searching for drug targets, and for biomarkers studies, it can be extremely rare to identify a single metabolite that will prove clinically viable, such that multiplexing is often required. The statistical techniques for the latter technique are manifold and beyond the scope of this paper.…”

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confidence: 99%

Applications of Metabolomics for Kidney Disease Research

Wettersten

Weiss

2013

Organogenesis

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Metabolomics is one of the relative newcomers of the omics techniques and is likely the one most closely related to actual real-time disease pathophysiology. Hence, it has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease. Despite this power, metabolomics as applied to kidney disease is still in its early adolescence and has not yet reached the mature stage of clinical application, i.e., specific biomarker and therapeutic target discovery. On the other hand, the insight gained from hints into what makes these diseases tick, as is evident from the metabolomics pathways which have been found to be altered in kidney cancer, are now beginning to bear fruit in leading to potential therapeutic targets. It is quite likely that, with greater numbers of clinical materials and with more investigators jumping into the field, metabolomics may well change the course of kidney disease research.

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confidence: 99%

Applications of Metabolomics for Kidney Disease Research

Wettersten

Weiss

2013

Organogenesis

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“…Several deconvolution methods, successfully applied in HPLC-DAD, are known and comprise both methods applied to single wavelength data and to multiwavelength data [11,12]. The first type of methods is based on assumed mathematical peak models, e.g.…”

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confidence: 99%

Efficient recovery of electrophoretic profiles of nucleoside metabolites from urine samples by multivariate curve resolution

et al. 2009

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Chemometric techniques usually employed in purity assessment and resolution of multicomponent peaks have been applied to analytical data from complex biological samples obtained with CE-DAD. In the assessment of the purity of the electrophoretic peaks, the orthogonal projection approach, the orthogonal projection approach with Durbin-Watson criterion, and the simple-to-use interactive self-modeling mixture analysis method have been employed. Multivariate curve resolution with alternating least squares has been successfully implemented to resolve co-migrating peaks of metabolites in CE-DAD and to recover qualitative and quantitative information about co-migrating components of urine extract. The main challenge consisted of developing high-quality multivariate curve resolution with alternating least squares models of multicomponent peaks acquired during the CE analysis of nucleoside patterns in 18 urine samples. The recovered ultraviolet visible (UV-Vis) spectra have been employed to identify additional nucleosides, such as 1-methylinosine, 2-methylguanosine, and 1-methylguanosine, whose presence in the metabolic profile produced by the applied CE-DAD method has not yet been recognized. Concentration profiles of these compounds can be used in metabonomic studies.

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“…Only such a deconvolution allows for the quantitative evaluation of the chromatogram that is required for process optimization. In the last decade, rather sophisticated tools were developed for the deconvolution of chromatographic peaks by other authors (see for instance [10,11]). Nevertheless, we prefer to use a simpler method in this study, which appears more suitable for the purpose of automated process optimization.…”

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High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems

et al. 2008

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Chromatographic processes can be optimized in various ways. However, the two most prominent approaches are either based on statistical data analysis or on experimentally validated simulation models. Both approaches heavily rely on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. Hence, here a closed-loop optimization strategy is proposed in that an automated high throughput liquid handling platform is combined with a genetic algorithm. This setup enables process optimization on the mini-scale and thus saves time as well as material costs. The practicability and robustness of the proposed high throughput method is demonstrated with two exemplary optimization tasks: first, optimization of the buffer composition in the capture step for a binary protein mixture (lysozyme and cytochrome), and second, optimization of multilinear gradient elution for the separation of a ternary mixture (ribonuclease and cytochrome, and lysozyme). IntroductionChromatography is widely used as a separation technique in the biotechnological industry. High selectivity and gentle conditions have made it an essential step in current purification processes for biological macromolecules, for instance, proteins. However, due to complex and dynamic interactions between protein molecules and adsorbent materials, the design of optimal separation processes is very difficult and time consuming. Heuristic design methods that are based on previous experiences with similar separation problems require a great amount of expert knowledge and usually do not lead to the global process optimum. Furthermore, process optimization is often restricted by time-to-market requirements and must, hence, be performed as fast as possible.Most methods for process optimization that are found in the literature today divide into two classes: model-based optimization and direct process optimization. In model-based optimization, mathematical models are utilized to mimic the studied processes. Optimization is performed in-silico and thus has the clear advantage of not restricting the optimization by lab schedules. Limiting factors are only computational effort and reliability or validity of the applied simulation models. The development of mechanistic models requires good process understanding, initial experiments for parameter estimation, and independent experiments for model validation. The latter is also true for black box models (for example [1,2]). The determination of mechanistic model parameters, such as effective mass transfer coefficients and isotherm coefficients, is generally very complex and requires large amounts of material and time, especially when the interactions of realistic multi-component mixtures are considered without significant model simplifications.An alternative to the model-based approach is to directly identify process optima based on the results of experiments that are iteratively planned by an optimization algorithm, such as repeated design of experiments (DoE) or an evolutionary strat...

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Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals

Cited by 136 publications

References 16 publications

Applications of Metabolomics for Kidney Disease Research

Applications of Metabolomics for Kidney Disease Research

Efficient recovery of electrophoretic profiles of nucleoside metabolites from urine samples by multivariate curve resolution

High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems

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